Publications (10)
CARLA: An Open Urban Driving Simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla +2
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autono…
Learned Image Compression for Machine Perception
Felipe Codevilla, Jean Gabriel Simard, Ross Goroshin +1
Recent work has shown that learned image compression strategies can outperform standard hand-crafted compression algorithms that have been developed over decades of intensive resea…
Action-Based Representation Learning for Autonomous Driving
Yi Xiao, Felipe Codevilla, Christopher Pal +1
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creati…
Single Image Restoration for Participating Media Based on Prior Fusion
Joel D. O. Gaya, Felipe Codevilla, Amanda C. Duarte +2
This paper describes a method to restore degraded images captured in a participating media -- fog, turbid water, sand storm, etc. Differently from the related work that only deal w…
Multimodal End-to-End Autonomous Driving
Yi Xiao, Felipe Codevilla, Akhil Gurram +2
A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressin…
Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction
Roger Girgis, Florian Golemo, Felipe Codevilla +5
Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a representation that approximates the true…
End-to-end Driving via Conditional Imitation Learning
Felipe Codevilla, Matthias Müller, Antonio López +2
Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be contro…
Scaling Vision-based End-to-End Driving with Multi-View Attention Learning
Yi Xiao, Felipe Codevilla, Diego Porres +1
On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., st…
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Felipe Codevilla, Eder Santana, Antonio M. López +1
Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation…
On Offline Evaluation of Vision-based Driving Models
Felipe Codevilla, Antonio M. López, Vladlen Koltun +1
Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vas…